class_docstr: Iterable dataset that returns constant length chunks of tokens from stream of text files.
      Args:
          tokenizer (Tokenizer): The processor used for proccessing the data.
          dataset (dataset.Dataset): Dataset with text files.
          infinite (bool): If True the iterator is reset after dataset reaches end else stops.
          seq_length (int): Length of token sequences to return.
          num_of_sequences (int): Number of token sequences to keep in buffer.
          chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.
          tokenized (bool): If true we use a pretokenized dataset.
code: def shuffle(self, buffer_size=1000):
      return ShufflerIterDataPipe(self, buffer_size=buffer_size)
  
imports: import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  import datasets
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  import transformers
  from accelerate import Accelerator, DistributedType
  from arguments import TrainingArguments
  from huggingface_hub import Repository
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
label_desc: Use image node_id 3 for calling the ConstantLengthDataset obj's underlying member method code with example usage: obj.shuffle(buffer_size) and returns: ShufflerIterDataPipe